Black woman working on computer, engineering, CodeGen Developer Challenge

How AI can help create a more inclusive recruitment process

Black woman working on computer, engineering

Article by Jen Shorten, Board Member at Clu

Organisations are facing record lows for talent retention. This issue largely stems from the fact that the focus on employee experience and success has been next to non-existent in most companies until very recently.

But this isn’t the only cause of the great resignation. Around 80% of employees don’t actually have the right skills for their jobs which leads to poor motivation, productivity, and job satisfaction. When layered against the reported success rate of recruitment sitting at just under 50% and a recent study by Checkster finding that 78% of people are lying on their CVs and cover letters when applying for roles, a case is evidenced that the current recruitment process is no longer accurate, fit for purpose or supporting talent retention.

Many sectors have proven that AI adds demonstrable value and can deliver exceptional results, so why isn’t the world of recruitment following suit in more effective ways? We have some thoughts.

AI in recruitment currently focuses on increasing the breadth of applications received and automating CV sifting using keyword-based algorithms. This is where AI can be used to make the most significant advancements, particularly in the way that we unearth talent, understand it, and set it up for success in our organisations.

The traditional process of scanning and filtering out CVs is rife with bias and anchors towards more of the same, instead of finding something different. According to one intensive academic study, minority applicants who ‘whitened’ their CVs were more than twice as likely to receive calls for interviews, and it did not matter whether the organisations they applied to claimed to value diversity or not.

AI can play a critical role in the democratisation of the job market and mitigating these entrenched biases.

It can be used to safeguard the inclusion of candidates from non-traditional backgrounds throughout the hiring process without needing to hide or erase their identity in the process. AI can help us understand candidates better and see them for what they can do, not just what they have or haven’t done previously to determine their relevancy for a role. It can also improve and enhance the way candidates and organisations find one another, improving the accuracy, experience and outcomes of recruitment for everyone.

Platforms such as Clu, will be pivotal in these advancements. To reduce bias, Clu removes CVs from the hiring process and helps organisations inclusively source and assess each candidate in consistent and transparent ways to create a more level playing field.  Over our years of research into building high-performing and diverse workforces, we have found that implementing hiring strategies that focus on both soft and technical skills and strategies that encourage collaboration between team members in the hiring process is the key to incremental improvement.

We believe it is important that AI is anchored to auditable, diverse and actionable data. Within our context, this ensures that recommendations to candidates and organisations are equitable, ethical and make it easier to mitigate and challenge unconscious and implicit bias. Our algorithm captures significantly more data sets across demographics, skills and target remuneration than other solutions in the Rec-tech market. We can use these new data points to allow organisations to track their performance in safeguarding holistic diversity and inclusion over time. Something a traditional CV-sifting algorithm could never do.

But AI driving better performance is only one part of the puzzle, it must also help with learning to be truly effective. We have spoken to over 1,000 hiring managers during our R&D phase and far too many couldn’t confidently dissect the soft and technical skills needed for a role. We can use AI to not only inform how skills can be matched to fill gaps and enhance teams, it can also be used to help hiring and recruitment managers create more attainable and actionable skills profiles to match candidates to.

Learning from the way people identify themselves, demographically and cognitively, can help elevate those who are systemically overlooked and excluded by standard recruitment to not only fill critical skills gaps currently facing organisations but also greater fulfil their potential.

We can improve the accuracy of recruitment by understanding more about what people from certain locations, with certain career and experience histories are most interested in. We can see what organisations are over indexing in their own cultures and start making recommendations of how they can better balance themselves to create the more holistic, innovative, and productive environments that indirectly impact recruitment and talent attraction.

An example that brings this to life is in an anecdote of a Harvard economics and law fellow Michael Rosenbaum sharing a new idea with the White House in the millennium. He argued that underserved, urban populations were an untapped source of talent for software development. The White House dismissed his idea, however, so he went on to found Catalyte which specialises in getting non-traditional talent into coding jobs and it is has gone on to have huge success.

We want to inspire policy makers and highlight how technology offers hope and positive conversation changes. We want to show that talent shines through in unexpected places when you look at what people can do.

Jen ShortenAbout the author

Jen has worked in software development for over 20 years specialising in large scale data integration and IT modernisation projects. She is on the Board of category-defining inclusive recruitment software, Clu. This cloud-based tool enriches every touchpoint of the hiring process for organisations, to hire the talent you want to and to build your team.